---
base_model: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16
language:
- en
- es
- fr
- de
- ja
- it
- zh
- ar
- he
- hi
- ko
- cs
- da
- nl
- fi
- pl
- pt
- th
- sv
- ru
library_name: transformers
license: other
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/
license_name: nvidia-nemotron-open-model-license
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
- nvidia
- pytorch
- nemotron-3
- latent-moe
- mtp
---
## About

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<!-- ### quants:  x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
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static quants of https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16

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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF).***

weighted/imatrix quants are available at https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-i1-GGUF
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.

## Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q2_K.gguf) | Q2_K | 53.6 |  |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q3_K_S.gguf) | Q3_K_S | 60.0 |  |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q3_K_M.gguf) | Q3_K_M | 67.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.IQ4_XS.gguf) | IQ4_XS | 67.8 |  |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q3_K_L.gguf) | Q3_K_L | 70.8 |  |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q4_K_S.gguf) | Q4_K_S | 75.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q4_K_M.gguf) | Q4_K_M | 86.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q5_K_S.gguf) | Q5_K_S | 86.9 |  |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q5_K_M.gguf) | Q5_K_M | 95.8 |  |
| [PART 1](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q6_K.gguf.part3of3) | Q6_K | 113.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16.Q8_0.gguf.part3of3) | Q8_0 | 128.6 | fast, best quality |

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

## FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.

## Thanks

I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.

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